Abstract Machine learning has been the corner stone in analysing and extracting information
from data and often a problem of missing values is encountered. Missing values occur …

The missing traffic data has caused great obstacles and interference to further research,
such as traffic flow prediction, which affects the traffic authorities' judgment for the real traffic …

Short‐term traffic forecasting is becoming more important in intelligent transportation
systems. The k‐nearest neighbour (kNN) method is widely used for short‐term traffic …

IoT devices are deployed in a building to instantly collect electricity load usage for next hour
load consumption forecasting so that the operation of the building can be properly managed …

Exploring complicated dynamic spatiotemporal correlations has always been a challenging
issue in traffic prediction. Besides, methods that make predictions directly from data with …

As an emerging paradigm, Cyber-Physical Social System (CPSS) can provide humans with
efficient, convenient and personalized services. However, due to the explosive growth of …

This work performs an in-depth study of the impact of distance functions on K-Nearest
Neighbours imputation of heterogeneous datasets. Missing data is generated at several …

Determining the occurrence of disinfection byproducts (DBPs) in drinking water distribution
system (DWDS) remains challenging. Predicting DBPs using readily available water quality …

Internet of Things (IoT) is emerging, and 5G enables much more data transport from mobile
and wireless sources. The data to be transmitted is too much compared to link capacity …

The imputation of time series is one of the most important tasks in the homogenization
process, the quality and precision of this process will directly influence the accuracy of the …